_____________________________________________________________________________________________________ ++ PG Scholar; # Associate Professor (SS&AC); † Director; ‡ Professor & Head; ^ Ph. D Scholar; *Corresponding author: E-mail: preethisekaragri@gmail.com; Int. J. Environ. Clim. Change, vol. 13, no. 10, pp. 571-582, 2023 International Journal of Environment and Climate Change Volume 13, Issue 10, Page 571-582, 2023; Article no.IJECC.104937 ISSN: 2581-8627 (Past name: British Journal of Environment & Climate Change, Past ISSN: 2231–4784) Comparing the Effectiveness of Different Machine Learning Algorithms for Crop Cover Classification Using Sentinel 2 S. Preethi a++* , Kumaraperumal Ramalingam a# , Sellaperumal Pazhanivelan b† , Dhanaraju Muthumanickam a‡ , Ragunath Kaliaperumal a# and Nivas Raj Moorthi a^ a Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore, India. b Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, India. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/IJECC/2023/v13i102688 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://www.sdiarticle5.com/review-history/104937 Received: 09/06/2023 Accepted: 12/08/2023 Published: 18/08/2023 ABSTRACT Crop cover mapping is an essential tool for controlling and enhancing agricultural productivity. By determining the spatial distribution of different crop types, solidified judgements regarding crop planning, crop management, and risk management can be made. Crop cover classification using optical data pose constraints in terms of spatial and spectral resolution. With Sentinel – 2 data providing the ground information at 10m resolution, users may choose the best spectral band Original Research Article